Hydrol. Earth Syst. Sci., 19, 747–770, 2015
www.hydrol-earth-syst-sci.net/19/747/2015/
doi:10.5194/hess-19-747-2015
© Author(s) 2015. CC Attribution 3.0 License.
Model study of the impacts of future climate change on the
hydrology of Ganges–Brahmaputra–Meghna basin
M. Masood1,2 , P. J.-F. Yeh3 , N. Hanasaki4 , and K. Takeuchi1
1 International
Centre for Water Hazard and Risk Management (ICHARM), PWRI, Tsukuba, Japan
Graduate Institute for Policy Studies (GRIPS), Tokyo, Japan
3 National University of Singapore, Singapore
4 National Institute for Environmental Studies, Tsukuba, Japan
2 National
Correspondence to: M. Masood ([email protected])
Received: 19 April 2014 – Published in Hydrol. Earth Syst. Sci. Discuss.: 2 June 2014
Revised: 28 December 2014 – Accepted: 7 January 2015 – Published: 4 February 2015
Abstract. The intensity, duration, and geographic extent of
floods in Bangladesh mostly depend on the combined influences of three river systems, the Ganges, Brahmaputra and
Meghna (GBM). In addition, climate change is likely to have
significant effects on the hydrology and water resources of
the GBM basin and may ultimately lead to more serious
floods in Bangladesh. However, the assessment of climate
change impacts on the basin-scale hydrology by using wellcalibrated hydrologic modeling has seldom been conducted
in the GBM basin due to the lack of observed data for calibration and validation. In this study, a macroscale hydrologic
model H08 has been applied over the basin at a relatively
fine grid resolution (10 km) by integrating the fine-resolution
DEM (digital elevation model) data for accurate river networks delineation. The model has been calibrated via the
analysis of model parameter sensitivity and validated based
on long-term observed daily streamflow data. The impacts
of climate change (considering a high-emissions path) on
runoff, evapotranspiration, and soil moisture are assessed by
using five CMIP5 (Coupled Model Intercomparison Project
Phase 5) GCMs (global circulation models) through three
time-slice experiments; the present-day (1979–2003), the
near-future (2015–2039), and the far-future (2075–2099) periods. Results show that, by the end of 21st century, (a) the
entire GBM basin is projected to be warmed by ∼ 4.3 ◦ C;
(b) the changes of mean precipitation (runoff) are projected
to be +16.3 % (+16.2 %), +19.8 % (+33.1 %), and +29.6 %
(+39.7 %) in the Brahmaputra, Ganges, and Meghna, respectively; and (c) evapotranspiration is projected to increase for
the entire GBM (Brahmaputra: +16.4 %, Ganges: +13.6 %,
Meghna: +12.9 %) due to increased net radiation as well
as warmer temperature. Future changes of hydrologic variables are larger in the dry season (November–April) than in
the wet season (May–October). Amongst the three basins,
the Meghna shows the highest increase in runoff, indicating
higher possibility of flood occurrence. The uncertainty due to
the specification of key model parameters in model predictions is found to be low for estimated runoff, evapotranspiration and net radiation. However, the uncertainty in estimated
soil moisture is rather large with the coefficient of variation
ranging from 14.4 to 31 % among the three basins.
1
Introduction
Bangladesh is situated in the active delta of three of the
world’s major rivers, the Ganges, Brahmaputra and Meghna.
Due to its unique geographical location, the occurrence of
water-induced disasters is a regular phenomenon. In addition,
the anticipated change in climate is likely to lead to an intensification of the hydrological cycle and to have a major impact on the overall hydrology of these basins and ultimately
lead to the increase in the frequency of water-induced disasters in Bangladesh. However, the intensity, duration and geographic extent of floods in Bangladesh mostly depend on the
combined influences of these three river systems. Previous
studies indicated that flood damages have become more severe and devastating when more than one flood peak in these
three river basins coincide (Mirza, 2003; Chowdhury, 2000).
Published by Copernicus Publications on behalf of the European Geosciences Union.
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M. Masood et al.: Hydrology of Ganges–Brahmaputra–Meghna basin
The Ganges–Brahmaputra–Meghna (hereafter referred to
as GBM) basin with a total area of about 1.7 million km2
(FAO-AQUASTAT, 2014; Islam et al., 2010) is shared by a
number of countries (Fig. 1). The Brahmaputra River begins
in the glaciers of the Himalayas and travels through China,
Bhutan, and India before emptying into the Bay of Bengal in
Bangladesh. It is snow-fed braided river and it remains a natural stream with no major hydraulic structures built along its
reach. The Ganges River originates at the Gangotri glaciers
in the Himalayas and it passes through Nepal, China and
India and empties into the Bay of Bengal at Bangladesh.
It is snowmelt-fed river and its natural flow is controlled
by a number of dams constructed by the upstream countries. The Meghna River is a comparatively smaller, rain-fed,
and relatively flashier river that runs through a mountainous
region in India before entering Bangladesh. Major characteristics of the GBM Rivers are presented in Table 1. This
river system is the world’s third largest freshwater outlet to
the oceans (Chowdhury and Ward, 2004). During extreme
floods, over 138 700 m3 s−1 of water flows into the Bay of
Bengal through a single outlet, which is the world largest
intensity, even exceeding that of the Amazon discharge by
about 1.5 times (FAO-AQUASTAT, 2014). The GBM basin
is unique in the world in terms of diversified climate. For
example, the Ganges River basin is characterized by low
precipitation (760–1020 mm year−1 ) in the northwestern upper region and high precipitation (1520–2540 mm year−1 )
along the coastal areas. High precipitation zones and dry rain
shadow areas are located in the Brahmaputra River basin,
whereas the world’s highest precipitation (11 871 mm year−1
at Mawsynram, Meghalaya state, India) area is situated in the
Meghna River basin (FAO-AQUASTAT, 2014).
Several studies have focused on the rainfall and discharge
relationships in the GBM basin by (1) identifying and linking the correlation between basin discharge and the El Niño–
Southern Oscillation (ENSO) and sea surface temperature
(SST) (Chowdhury and Ward, 2004; Mirza et al., 1998;
Nishat and Faisal, 2000), (2) analyzing available observed
or reanalysis data (Chowdhury and Ward, 2004, 2007; Mirza
et al., 1998; Kamal-Heikman et al., 2007), and (3) evaluating historical data of flood events (Mirza, 2003; Islam et al.,
2010). Various statistical approaches were used in the above
studies instead of using hydrologic model simulations. In recent years, a number of global-scale hydrologic model studies (Haddeland et al., 2011, 2012; Pokhrel et al., 2012) have
been reported. Although their modeling domains include the
GBM basin, these global-scale simulations are not fully reliable due to the lack of model calibration at both the global
and basin scales.
Few studies have been conducted to investigate the impact
of climate change on the hydrology and water resources of
the GBM basin (Immerzeel, 2008; Kamal et al., 2013; Biemans et al., 2013; Gain et al., 2011; Ghosh and Dutta, 2012).
In most of these studies, future streamflow is projected on
the basis of linear regression between rainfall and streamflow
Hydrol. Earth Syst. Sci., 19, 747–770, 2015
derived from historical data (Immerzeel, 2008; Chowdhury
and Ward, 2004; Mirza et al., 2003). Immerzeel (2008) used
the multiple regression technique to predict streamflow at the
Bahadurabad station (the outlet of Brahmaputra Basin) under future temperature and precipitation conditions based on
a statistically downscaled GCM (global circulation model)
output. However, since most hydrologic processes are nonlinear, they cannot be predicted accurately by extrapolating
empirically derived regression equations to the future projections. The alternative for the assessment of climate change
impacts on basin-scale hydrology is via well-calibrated hydrologic modeling, but this has rarely been conducted for the
GBM basin due to the lack of observed data for model calibration and validation. Ghosh and Dutta (2012) applied a
macroscale distributed hydrologic model to study the change
of future flood characteristics at the Brahmaputra Basin, but
their study domain is only focused on the regions inside India. Gain et al. (2011) estimated future trends of the low
and high flows in the lower Brahmaputra Basin using outputs
from a global hydrologic model (grid resolution: 0.5◦ ) forced
by multiple GCM outputs. Instead of model calibration, the
simulated future streamflow is weighted against observations
to assess the climate change impacts.
In this study, a hydrologic model simulation is conducted
in which the calibration and validation is based on a rarely
obtained long-term (1980–2001) observed daily streamflow
data set in the GBM basin provided by the Bangladesh Water Development Board (BWDB). Relative to previous GBM
basin studies, it is believed that the availability of this unique
long-term streamflow data can lead to more precise estimation of model parameters and hence more accurate hydrological simulations and more reliable future projection of the
hydrology over the GBM basin.
The objective of this study is to (1) setup a hydrologic
model for the GBM basin and calibrate and validate the
model with the long-term observed daily streamflow data,
and (2) to study the impact of future climate changes on
the basin-scale hydrology. A global hydrologic model H08
(Hanasaki et al., 2008, 2014) is applied regionally over the
GBM basin at a relatively fine grid resolution (10 km) by
integrating the fine-resolution (∼ 0.5 km) DEM (digital elevation model) data for the accurate river networks delineation. The hourly atmospheric forcing data from the Water and Global Change (WATCH) model-intercomparison
project (Weedon et al., 2011) (hereafter referred to as WFD,
i.e., WATCH forcing data set) are used for the historical simulations. WFD is considered as one of the best available
global climate forcing data sets to provide accurate representation of meteorological events, synoptic activity, seasonal
cycles and climate trends (Weedon et al., 2011). The studies by Lucas-Picher et al. (2011) and Siderius et al. (2013)
found that for southern Asia and the Ganges, respectively, the
WFD rainfall is consistent with the APHRODITE (Yatagai et
al., 2012), a gridded (0.25◦ ) rainfall product for the southern
Asia region developed based on a large number of rain gauge
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M. Masood et al.: Hydrology of Ganges–Brahmaputra–Meghna basin
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Table 1. Major characteristics of the Ganges, Brahmaputra and Meghna river basins.
Item
Brahmaputra
Ganges
Meghna
Basin area (km2 )
583 000b
530 000f,g
543 400h
907 000b
65 000b
82 000h
1800b
2900f
2896a
2000b
2510c
2500a
946b
River length (km)
1 087 300h
1 000 000c
Elevation (m a.s.l.)e
Range
Average
Area below 500 m:
Area above 3000 m:
8 ∼ 7057
3141
20 %
60 %
3 ∼ 8454
864
72 %
11 %
−1 ∼ 2579
307
75 %
0%
Discharge (m3 s−1 )
Station
Lowest
Highest
Average
Bahadurabad
3430d
102 535d
20 000g
Hardinge Bridge
530d
70 868d
11 300d
Bhairab Bazar
2d
19 900d
4600d
Land use (% area)i
Agriculture
Forest
19 %
31 %
68 %
11 %
27 %
54 %
Basin-averaged normalized difference
vegetation index (NDVI)j
0.38
0.41
0.65
Total number of dams (both for
hydropower and irrigation purpose)k
6
75
–
a Moffitt et al. (2011). b Nishat and Faisal (2000). c Abrams (2003). d BWDB (2012). e Estimated from SRTM DEM data by Lehner et al. (2006). f
Gain et al. (2011). g Immerzeel (2008). h FAO-AQUASTAT (2014). i Estimated from Tateishi et al. (2014). j Estimated from NEO (2014). k Lehner
et al. (2008).
data. For future simulations, the H08 model is forced by climate model output under the high-emissions scenario (RCP
8.5 – representative concentration pathway) from five different coupled atmosphere–ocean GCMs, all of which participate in the Coupled Model Intercomparison Project Phase
5 (CMIP5) (Taylor et al., 2012). In order to be consistent
with the historical data, for each basin the monthly correction factor (i.e., the ratio between the monthly precipitation of the WFD data and that of the GCM data for each
month) is applied to the GCM’s future precipitation outputs.
Three time-slice experiments are performed for the presentday (1979—2003), the near-future (2015–2039), and the farfuture (2075—2099) periods.
Our present modeling study makes advances over previous similar studies in three aspects. First, the H08 model has
been demonstrated as a suitable tool for large-scale hydrologic modeling (Hanasaki et al., 2008) and, in this study, it is
first calibrated via analyzing model parameter sensitivity in
the GBM basin before being validated against the observed
long-term daily streamflow data set. Second, the uncertainty
due to the determination of model parameters in hydrologic
simulations, which is seldom considered in previous studies,
is analyzed intensively in this study. Third, three large GBM
basins and their spatial variability are studied, respectively, in
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this study via an integrated model framework which benefits
the analysis of the combined influences of the three rivers on
the large-scale floods and droughts in Bangladesh, as extensively reported in literature (Chowdhury, 2000; Mirza, 2003).
Finally, the impacts of climate change not only on streamflow, but also on other hydro-meteorological variables, including evapotranspiration, soil moisture and net radiation,
are also assessed in this study, unlike in most previous studies where the climate change impact on streamflow is often
the only focus.
The paper is organized into five sections as follows. A brief
description of the data and hydrologic model used is presented in Sect. 2. Section 3 presents the model setup as well
as the results from the model parameter sensitivity analysis.
Results and discussion are presented in Sect. 4, and important conclusions of this study are summarized in Sect. 5.
2
Data and tools
Basic information and characteristics (type, source, resolution and periods of data) of input data used in this study are
summarized in Table 2.
Hydrol. Earth Syst. Sci., 19, 747–770, 2015
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M. Masood et al.: Hydrology of Ganges–Brahmaputra–Meghna basin
Figure 1. The boundary of the Ganges–Brahmaputra–Meghna (GBM) basin (thick red line), the three outlets (red star): Hardinge Bridge,
Bahadurabad and Bhairab Bazar for the Ganges, Brahmaputra and Meghna river basins, respectively. Green stars indicate the locations of
three additional upstream stations: Farakka, Pandu and Teesta (modified from Pfly, 2011).
2.1
Meteorological forcing data sets
The WFD (Weedon et al., 2011) is used to drive the H08
model for the historical simulation. The WFD variables, including rainfall, snowfall, surface pressure, air temperature,
specific humidity, wind speed, long-wave downward radiation, and shortwave downward radiation were taken from
the ERA-40 reanalysis product of the European Centre for
Medium Range Weather Forecasting (ECMWF). The ERA
reanalysis data with the 1 ◦ resolution were interpolated into
the 0.5 ◦ resolution on the Climate Research Unit of the University of East Anglia (CRU) land mask, adjusted for elevation changes where needed and bias-corrected using monthly
observations. For detailed information on the WFD, see Weedon et al. (2011, 2010). The albedo values are based on the
monthly albedo data form the Second Global Soil Wetness
Project (GSWP2).
nels. In contrast, the Meghna River at Bhairab Bazar is seasonally tidal – after withdrawal of the monsoon the river near
this station becomes tidal, and from December to May the
river shows both a horizontal and a vertical tide (Chowdhury
and Ward, 2004). Under this condition, during the dry season tidal discharge measurements were made at this station
once per month. Daily discharges of Ganges and Brahmaputra rivers were calculated from the daily water level data
by using the rating equations developed by the Institute of
Water Modelling (IWM) (IWM, 2006). A rating equation for
the Meghna River is not reported in literature. In this study
an attempt was made to develop the rating equation for the
Meghna Basin. Discharge (monthly) data of another three
stations (Farakka, Pandu, Teesta) located upstream of these
basins (Fig. 1) were collected from the Global Runoff Data
Centre (GRDC) and were also useful for model validation
purposes.
2.2
2.3
Hydrologic data
Observed river water level (daily) and discharge (weekly)
data from 1980 to 2012 for the hydrological stations located
inside the Bangladesh (the outlets of three basins shown
in Fig. 1, i.e., the Ganges Basin at Hardinge Bridge, the
Brahmaputra Basin at Bahadurabad, and the Meghna Basin
at Bhairab Bazar) were provided by the Hydrology Division,
Bangladesh Water Development Board (BWDB). River water levels were regularly measured 5 times a day (at 06:00,
09:00, 12:00, 15:00 and 18:00 local time) and discharges
were measured weekly by the velocity–area method. Since
the Brahmaputra River is highly braided, the discharge measurements at Bahadurabad were carried out on multiple chanHydrol. Earth Syst. Sci., 19, 747–770, 2015
Topographic data
DEM data were collected from HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at
multiple Scales) (HydroSHEDS, 2014). It offers a suite of
georeferenced data sets (vector and raster), including stream
networks, watershed boundaries, drainage directions, and ancillary data layers such as flow accumulations, distances and
river topology information (Lehner et al., 2006). The HydroSHEDS data were derived from the elevation data of the
Shuttle Radar Topography Mission (SRTM) at a ∼ 0.5 km
resolution. Preliminary quality assessments indicate that the
accuracy of HydroSHEDS significantly exceeds that of existing global watershed and river maps (Lehner et al., 2006).
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M. Masood et al.: Hydrology of Ganges–Brahmaputra–Meghna basin
2.4
GCM data
Climate data from five CMIP5 climate models – MIROC5,
MIROC-ESM, MRI-CGCM3, HadGEM2-ES (under the
RCP 8.5) and MRI-AGCM3.2S (under the SRES A1B – Special Report on Emissions Scenarios) – are used in this study
as the forcing data for future hydrological simulations (see
Appendix B, Table B1). The climate data have been interpolated from their original climate model resolutions (ranging from 0.25 × 0.25◦ to 2.8 × 2.8◦ ) to 50 × 50 (∼ 10 kmmesh) using linear interpolation (nearest four-point). In order to be consistent with the historical simulation forced by
WFD, the precipitation forcing data in each GBM basin from
each GCM are corrected by multiplying a monthly correction factor, which is equal to the ratio between the basinaveraged long-term mean precipitation from WFD and that
from each GCM for all the months. Among these GCMs,
MRI-AGCM3.2S (where the “S” refers to the “super-high
resolution”) provides higher-resolution (20 km) atmospheric
forcing data which shows improvements in simulating heavy
precipitation, global distribution of tropical cyclones, and the
seasonal march of the east Asian summer monsoon (Mizuta
et al., 2012). The MRI-AGCM3.2S forcing data set has been
used in several recent climate change impact studies focused
on southern Asia (Rahman et al., 2012; Endo et al., 2012;
Kwak et al., 2012).
2.5
Hydrologic model: H08
H08 is a macroscale hydrological model developed by
Hanasaki et al. (2008) which consists of six main modules:
land surface hydrology, river routing, crop growth, reservoir
operation, environmental flow requirement estimation, and
anthropogenic water withdrawal. For this study, only two
modules, the land surface hydrology and the river routing are
used. The land surface hydrology module calculates the energy and water budgets above and beneath the land surface as
forced by the high-temporal-resolution meteorological data.
The runoff scheme in H08 is based on the bucket model
concept (Manabe, 1969) but differs from the original formulation in certain important aspects. Although runoff is generated only when the bucket is overfilled as in the original bucket model, H08 uses a “leaky bucket” formulation
in which subsurface runoff occurs continually as a function
of soil moisture. Soil moisture is expressed as a single-layer
reservoir with the holding capacity of 15 cm for all the soil
and vegetation types. When the reservoir is empty (full), soil
moisture is at the wilting point (the field capacity). Evapotranspiration is expressed as a function of potential evapotranspiration and soil moisture (Eq. 2). Potential evapotranspiration and snowmelt are calculated from the surface energy balance (Hanasaki et al., 2008).
Potential evaporation EP is expressed in this model as
EP (TS ) = ρCD U (qSAT (TS ) − qa ),
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(1)
751
where ρ is the density of air, CD is the bulk transfer coefficient, U is the wind speed, qSAT (TS ) is the saturated specific
humidity at surface temperature, and qa is the specific humidity. Evaporation from a surface (E) is expressed as
(2)
E = βEP (TS ),
where
1
β=
W/Wf
0.75Wf ≤ W
,
W < 0.75Wf
(3)
where W is the soil water content and Wf is the soil water
content at field capacity (fixed at 150 kg m−2 ).
Surface runoff (Qs ) is generated whenever the soil water
content exceeds the field capacity:
W − Wf
Wf < W
Qs =
.
(4)
0
W ≤ Wf
Subsurface runoff (Qsb ) is incorporated to the model as
Wf W γ
,
(5)
Qsb =
τ Wf
where τ is a time constant and γ is a parameter characterizing the degree of nonlinearity of Qsb . These two parameters
are calibrated in this study as described later in Sect. 3.1.
The river module is identical to the Total Runoff Integrating Pathways (TRIP) model (Oki and Sud, 1998). The module has a digital river map covering the whole globe at a spatial resolution of 1◦ (∼ 111 km). The land–sea mask is identical to the GSWP2 meteorological forcing input. Effective
flow velocity and meandering ratio are set as the default values at 0.5 m s−1 and 1.5, respectively. The module accumulates runoff generated by the land surface model and routes
it downstream as streamflow. However, for this study a new
digital river map of the GBM basin with the spatial resolution
of ∼ 10 km is prepared. Effective flow velocity and meandering ratio have been calibrated, respectively, for the three
basins.
3
Methodology: model setup and simulation
Figure 2 presents the methodology used in this study from
model setup to the historical and future simulations. The
H08 simulation with the 10 km (50 ) resolution is calibrated
to find the optimal parameter sets by using the parametersampling simulation technique, and validated with observed
daily streamflow data. The default river module of H08 uses
the digital river map from TRIP (Oki and Sud, 1998) with the
global resolution of 1◦ (∼ 111 km) which is too coarse for the
regional simulation in this study, which has the 10 km resolution. Therefore, a new digital river map of the 10 km resolution is prepared for this purpose by integrating the finerresolution (∼ 0.5 km) DEM data.
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M. Masood et al.: Hydrology of Ganges–Brahmaputra–Meghna basin
Figure 2. Flow chart of the methodology used in this study.
3.1
Parameter sensitivity
The parameter-sampling simulation is conducted to investigate the sensitivity of the H08 model parameters to simulation results. The most sensitive parameters in H08 include
the root-zone depth d (m), the bulk transfer coefficient CD
(–) controlling the potential evaporation (Eq. 1), and the parameters sensitive to subsurface flow, that is, τ (day) and γ
(–) (Eq. 5) (Hanasaki et al., 2014); hence, they are treated
as calibration parameters in this study. The parameter τ is
a time constant determining the daily maximum subsurface
runoff. The parameter γ is a shape parameter controlling
the relationship between subsurface flow and soil moisture
(Hanasaki et al., 2008). Their default parameter values in
H08 are 1 m for d, 0.003 for CD , 100 days for τ , and 2 for
γ . For each of these four parameters, five different values are
selected from their feasible physical ranges. The parametersampling simulations of the H08 model were run by using all
the combinations of four parameters, which consist of a total
of 54 (625) simulations conducted by using the same 11-year
(1980—1990) atmospheric forcing data of WFD.
Figure 3 plots the 11-year long-term average seasonal cycles of simulated total runoff, surface runoff and subsurface
runoff of the Brahmaputra Basin. Each of the five lines in
each panel represents the average of 53 (125) runs with one
of the four calibration parameters fixed at a given value. As
shown, the overall sensitivity of the selected model parameters to the flow partitioning is high. When d is low, surface runoff is high (due to higher saturated fractional area)
(Fig. 3b). As d increases, subsurface runoff increases and
surface runoff decreases (Fig. 3c and b). Due to these compensating effects, the effect of d on the total runoff becomes
more complex: from March to August higher d causes lower
total runoff, but the trend is reversed from August on for the
Hydrol. Earth Syst. Sci., 19, 747–770, 2015
Brahmaputra Basin. Similar behaviors can be observed for
the other two basins (figure not shown).
The parameter CD is the bulk transfer coefficient in the
calculation of potential evaporation (Eq. 1); thus, its effect
on runoff is relatively small (Fig. 3d–f). However, higher CD
causes more evaporation and hence lower (both surface and
subsurface) runoff (Eqs. 1, 2). The sensitivity of parameter γ
to runoff is also smaller than d and τ . As γ increases, surface
runoff increases and subsurface runoff decreases (Fig. 3h, i).
The overall sensitivity of γ to the total runoff becomes negligible due to the compensating effects (Fig. 3g).
As shown in Eq. (5) and Fig. 3k–l, the parameter τ has a
critical impact on the surface and subsurface flow partitioning. A larger τ corresponds to larger surface runoff and hence
smaller subsurface runoff (Fig. 3k–l), but it has relatively a
small impact on total runoff (Fig. 3j).
These four calibration parameters have a combined influence on total runoff partitioning as well as on the simulations
of other hydrologic variables. To summarize, (1) the sensitivity of d on the total runoff is complex, i.e., the trend is
reversed between the two halves of a year; (2) parameters d
and τ have a significant impact on flow partitioning whereas
CD and γ have less sensitivity to runoff simulation; and(3)
the influence of d and τ is reversed between surface and subsurface runoff: surface runoff increases as d decreases and τ
increases.
Figure 4e plots the uncertainty bands of the simulated
discharges by using 10 optimal parameter combinations according to the Nash–Sutcliffe coefficient of efficiency (NSE)
(Nash and Sutcliffe, 1970). It is observed that the spread
of the uncertainty band is located mainly around the low
flow period (dry season from November to March) over the
Brahmaputra Basin (Fig. 4e). No surface runoff is generated
in the dry season when the soil moisture is lower than the
field capacity (Eq. 4 and Fig. 3b). It is noted from the 10
optimal parameter combinations that the optimal τ is 150,
CD is 0.001, d and γ range from 3 to 5 and 1.0 to 2.5, respectively. The spread of the uncertainty bands is mainly due
to the variations of d and γ . As d increases, the subsurface
runoff increases (Figs. 3c, 4e). On the other hand, in the case
of the Ganges and Meghna basins the spread of uncertainty
bands are observed through the entire period of a year (in low
flow as well as in peak flow regimes). Among the 10 optimal
parameter combinations for the Ganges (Meghna) it is found
that parameter CD is 0.008 (0.008), τ is 150 (50), d and γ
range from 4 to 5 (4 to 5) and 2.5 to 4 (1.5 to 2), respectively.
In the dry period, when surface runoff is nearly zero, subsurface runoff increases as d increases. A higher CD causes
higher evaporation which influences runoff as well (Eq. 1).
As discussed earlier, the influence of d on the total runoff is
complex, which results in the variation of simulated runoff
throughout the year. The spread of the uncertainty bands is
large in the peak flow period as the sensitivity of both surface and subsurface runoff is also large with respect to the
value of d (not shown).
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M. Masood et al.: Hydrology of Ganges–Brahmaputra–Meghna basin
753
Figure 3. The 11-year (1980–1990) mean seasonal cycles of the simulated total runoff, surface runoff and subsurface runoff (mm day−1 ) in
the Brahmaputra Basin. Each of the five lines in each panel represents the average of 53 (125) runs with one of the four calibration parameters
fixed at a given reasonable value.
3.2
Calibration and validation
The historical simulation from 1980 to 2001 is divided into
two periods with the first half (1980–1990) as the calibration
period and the second half (1991–2001) as validation. Basic information and characteristics (location, drainage area,
and periods of available observed data) of the six validation stations in the GBM basin are summarized in Table 3.
Model performance is evaluated by comparing observed and
simulated daily streamflow by the NSE (Nash and Sutcliffe,
1970), the optimal objective function for assessing the overall fit of a hydrograph (Sevat and Dezetter, 1991). A series of
sensitivity analysis of H08 parameters was conducted from
which 10 sets of optimal parameters are determined by using the parameter-sampling simulation as discussed earlier;
these parameter sets are used to quantify the uncertainty in
both historical and future simulations in the following. Figure 4 plots the daily hydrograph comparisons at the outlets
of the three river basins with the corresponding daily observations for both calibration and validation periods. The
obtained NSE for the calibration (validation) period is 0.84
(0.78), 0.80 (0.77), and 0.84 (0.86), while the percent bias
(PBIAS) is 0.28 % (6.59 %), 1.21 % (2.23 %) and −0.96 %
(3.15 %) for the Brahmaputra, Ganges, and Meghna basins,
respectively. For all basins, the relative root-mean-square error (RRMSE), the correlation coefficient (cc), and the coefficient of determination (R 2 ) for the calibration (validation)
period range from 0.32 to 0.60 (0.32 to 0.59), 0.91 to 0.93
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(0.89 to 0.94) and 0.82 to 0.86 (0.79 to 0.88), respectively.
These statistical indices (Table 4) suggest that the model performance is overall satisfactory. To further evaluate model
performance at upstream stations, the monthly discharge data
at three upstream stations (Farakka, Pandu, Teesta) collected
from the GRDC are used to compare with model simulations, and the result shows that the mean seasonal cycle of
simulated streamflow matches well with the corresponding
GRDC observations in these three upstream stations (see Appendix A).
4
Results and discussion
The calibrated H08 model is applied to the simulations
for the following three time-slice periods, the present
(1979–2003), the near-future (2015–2039), and the far-future
(2075–2099) period. For the present simulation, both WFD
and GCM climate forcing data are used. For the future simulation, only GCM forcing data are used. Simulation results
for the two future periods are then compared with the present
period (1979–2003) simulation forced by a GCM to assess
the effect of climate change on the hydrology and water resources of the GBM basin in terms of precipitation, air temperature, evapotranspiration, soil moisture and net radiation.
The results are presented in the following.
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M. Masood et al.: Hydrology of Ganges–Brahmaputra–Meghna basin
Figure 4. The simulated discharges (red line) using the WFD forcing data (both calibration and validation period) compared with observations
(green line) at outlets of the (a) Brahmaputra, (b) Ganges, and (c) Meghna rivers, (d) mean monthly (1980–2001) simulated discharges
compared with those of observations at outlets; (e) simulated discharges by using the 10 optimal parameter sets (red line) and the associated
uncertainty bands (green shading) in a typical year (1985). NSE, PBIAS, RRMSE, cc and R 2 for both calibration and validation periods are
noted at subplots (a), (b) and (c).
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Table 2. Basic input data used in this study.
Type
Description
Source/reference(s)
Original spatial resolution
Period
Remarks
Physical data
Digital elevation map
(DEM)
HydroSHEDSa
(HydroSHEDS, 2014)
1500 (∼ 0.5 km)
–
Global data
Basin mask
HydroSHEDSa
(HydroSHEDS, 2014)
3000 (∼ 1 km)
–
Rainfall, snowfall,
surface pressure, air
temperature, specific
humidity, wind speed,
long-wave downward
radiation, shortwave
downward radiation
WFDb
(Weedon et al., 2010,
2011)
0.5◦
1980–2001
50 (∼ 10 km mesh) data has been
prepared by linear interpolating
for this study.
albedo
GSWP2c
1◦
1980–1990
Mean monthly 50 (∼ 10 km mesh)
data has been prepared for this
study.
Water level
discharge
Bangladesh Water
Development Board
(BWDB)
Gauged
1980–2012
Discharge
Global Runoff Data
Centre (GRDC)
Gauged
1949–1973 (Farakka),
1975–1979 (Pandu),
1969–1992 (Teesta)
with missing data
Water level (daily), discharge
(weekly) data at outlets of three
basins, i.e., the Ganges Basin at
Hardinge Bridge, the Brahmaputra Basin at Bahadurabad, and the
Meghna Basin at Bhairab Bazar
obtained from BWDB.
Discharge (monthly) data at three
upstream stations, i.e., at Farakka
(Ganges), Pandu (Brahmaputra)
and Teesta (Brahmaputra).
Rainfall, snowfall,
surface pressure, air
temperature, specific
humidity, wind speed,
long-wave downward
radiation, shortwave
downward radiation
MRI-AGCM3.2Sd
0.25◦ (∼ 20 km-mesh)
1979–2003, 2015–
2039, 2075–2099
MIROC5
MIROC-ESM
MRI-CGCM3
HadGEM2-ES
1.41 × 1.39◦
2.81 × 2.77◦
1.125 × 1.11◦
1.875 × 1.25◦
Meteorological data
Hydrologic data
GCM data
Bias of precipitation data set
has been corrected by multiplying by the monthly correction
coefficient (ratio between basinaveraged long-term monthly
mean precipitation from WFD
and that from each GCM) for
each GBM basins.
a HydroSHEDS is Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales, b WFD is WATCH forcing data, c GSWP2 is Second Global Soil Wetness Project, d MRI-AGCM is
Meteorological Research Institute-Atmospheric General Circulation Model.
Table 3. Basic information of the streamflow validation stations in the GBM basin.
Basin name
4.1
Brahmaputra
Ganges
Meghna
Station name
Bahadurabad
Pandu
Teesta
Hardinge Bridge
Farakka
Bhairab Bazar
Latitude
Longitude
Drainage area (km2 )
Available observed
data period (with missing)
25.18◦ N
89.67◦ E
583 000
1980–2001
26.13◦ N
91.7◦ E
405 000
1975–1979
25.75◦ N
89.5◦ E
12 358
1969–1992
24.08◦ N
89.03◦ E
907 000
1980–2001
25◦ N
87.92◦ E
835 000
1949–1973
25.75◦ N
89.5◦ E
65 000
1980–2001
Seasonal cycle
Figure 5 plots the 22-year (1980–2001) mean seasonal cycles
of the climatic (from WFD forcing) and hydrologic (from
model simulations) quantities averaged over the three basins
(the corresponding mean annual amounts of these variables
are presented in Table 5). Also given in Fig. 5 is the box-andwww.hydrol-earth-syst-sci.net/19/747/2015/
whisker plot showing the range of variability for each month.
The interannual variation of precipitation in the Brahmaputra and Meghna basins is high from May to September
(Fig. 5a, c), whereas in the Ganges it is from June to October. However, the magnitude of precipitation differs substantially among the three basins. The Meghna Basin has a
significantly higher precipitation than the other two basins
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M. Masood et al.: Hydrology of Ganges–Brahmaputra–Meghna basin
Table 4. Statistical indices that measure the model performance in the three basins (GBM) during both calibration and validation periods.
Statistical indices
Brahmaputra
Ganges
Meghna
Calibration
Validation
Calibration
Validation
Calibration
Validation
0.84
0.28 %
0.32
0.93
0.86
0.78
6.59 %
0.38
0.89
0.79
0.80
1.21 %
0.60
0.91
0.82
0.77
2.23 %
0.59
0.89
0.79
0.84
0.96 %
0.38
0.93
0.86
0.86
3.15 %
0.32
0.94
0.88
Nash–Sutcliffe efficiency (NSE)
Percent bias (PBIAS)
Root-Mean Square Error (RRMSE)
Correlation coefficient (cc)
Coefficient of determination (R 2 )
Table 5. The 22-year (1980–2001) averages of the meteorological (from the WFD forcing data) and hydrologic variables in the GBM river
basins.
Unit
Brahmaputra
Ganges
Meghna
mm year−1
◦C
W m−2
g kg−1
1609
9.1
31
9.3
1157
21.7
74
11.8
3212
23.0
84
14.4
mm year−1
mm year−1
mm year−1
1360
251
415
406
748
2359
2193
1000
1689
(a) Meteorological variables
Precipitation (Prcp)
Temperature (Tair )
Net radiation (Net rad)
Specific humidity
(b) Hydrological variables
Runoff
Evapotranspiration (ET)
Potential Evapotranspiration (PET)
(Table 5); also, the maximum (monthly) precipitation during
1980–2001 occurs in May with a magnitude of 32 mm day−1 ,
while that in the Brahmaputra and Ganges occur in July
with the magnitudes of 15 and 13 mm day−1 , respectively.
Moreover, the seasonality of runoff in all three basins corresponds well with that of precipitation. Runoff (Fig. 5j–l)
in the Ganges Basin is much lower (the monthly maximum
of 4.3 mm day−1 in August) than in the other two basins
(a monthly maximum of 9.3 mm day−1 in the Brahmaputra
and 15.9 mm day−1 in the Meghna, both in July). In addition, ET (evapotranspiration) in the Brahmaputra Basin is
significantly lower (251 mm year−1 ) than in the other two
basins (748 mm year−1 in Ganges and 1000 mm year−1 in
Meghna). The contrasting ET magnitudes among the three
basins are due to multiple reasons: differences in elevation,
amounts of surface water to evaporate, air temperature, and
possibly wind and solar irradiance situations. Lower ET in
the Brahmaputra Basin is likely due to its cooler air temperature, higher elevation and less vegetated area. The basinaverage normalized difference vegetation index (NDVI) in
the Brahmaputra is 0.38, whereas in the Ganges and Meghna
NDVI is 0.41 and 0.65, respectively (NEO, 2014). However,
the patterns of seasonal ET variability in the Brahmaputra
and Meghna basins are quite similar, except there is a drop in
July in the Brahmaputra (Fig. 5m–o). ET is relatively stable
from May to October in the Brahmaputra and Meghna basins
in contrast to that in the Ganges where ET does not reach the
Hydrol. Earth Syst. Sci., 19, 747–770, 2015
peak until September. Finally, both the pattern and magnitude of seasonal soil moisture variations are rather different
among the three basins (Fig. 5p–r). However, the peak of soil
moisture occurs consistently in August in all three basins.
Figure 5d–f present the 22-year mean seasonal cycle of
basin-average air temperature (Tair ). The Brahmaputra Basin
is much cooler (mean temperature 9.1 ◦ C) than the Ganges
(21.7 ◦ C) and the Meghna (23.0 ◦ C). Figure 5g–i plot the
mean seasonal cycle of net radiation averaged over the
three basins. The seasonal pattern of net radiation is similar, but the magnitudes differ significantly among the three
basins: the average net radiation is ∼ 31, 74 and 84 W m−2 in
the Brahmaputra, Ganges and Meghna basins, respectively,
while the maximum (monthly-average) net radiation is ∼ 47,
100 and 117 W m−2 , respectively, in these three basins (Table 5).
4.2
Correlation between meteorological and
hydrological variables
Figure 6 presents the scatter plots and correlation coefficients between monthly meteorological and hydrological
variables in three river basins. Three different colors represent three different seasons: dry/winter (November–March),
pre-monsoon (April–June), and monsoon (July–October).
From this plot, the following summary can be drawn. Total runoff and surface runoff of the Brahmaputra Basin have
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M. Masood et al.: Hydrology of Ganges–Brahmaputra–Meghna basin
757
Figure 5. (a)–(r) Seasonal cycle of climatic and hydrologic quantities during 1980–2001. Box-and-whisker plots indicate minimum and
maximum (whiskers), 25th and 75th percentiles (box ends), and median (black solid middle bar). Solid curve line represents the interannual
average value. All abbreviated terms here refer to Table 5.
stronger correlation (cc = 0.95 and 0.97, both are statistically significant at p < 0.05) with precipitation than the other
two basins. However, subsurface runoff in the Brahmaputra Basin has weaker correlation (cc = 0.62, p < 0.05) with
precipitation than that in the Ganges (cc = 0.75, p < 0.05)
and Meghna (cc = 0.77, p < 0.05). These relationships im-
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ply that the deeper soil depths enhance the correlation between subsurface runoff and precipitation. The deeper rootzone soil depth (calibrated d = 5 m) in the Meghna Basin
generates more subsurface runoff (69 % of total runoff) than
other two basins. Soil moisture in the Meghna Basin also
shows stronger correlation (cc = 0.87, p < 0.05) with pre-
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M. Masood et al.: Hydrology of Ganges–Brahmaputra–Meghna basin
Ganges
Brahmaputra
Meghna
cc: 0.95
cc: 0.83
cc: 0.87
cc: 0.97
cc: 0.86
cc: 0.85
cc: 0.62
cc: 0.75
cc: 0.77
cc: 0.77
cc: 0.82
cc: 0.87
cc: 0.70
cc: 0.45
cc: 0.61
cc: 0.89
cc: 0.29
cc: 0.80
cc: 0.84
cc: 0.59
cc: 0.80
cc: 0.89
cc: 0.34
cc: 0.44
dry/winter (November-March)
pre-monsoon (April-June)
monsoon (July-October)
Figure 6. The correlation between the monthly means of meteorological variables (WFD) and that of hydrological variables for the Brahmaputra, Ganges and Meghna basins. Three different colors represent the data in three different seasons. Black: dry/winter (November–March);
green: pre-monsoon (April–June); red: monsoon (July–October). The cc for each pair (all three seasons together) is noted at each subplot.
The units are millimeters per day for Prcp, ET, and runoff; millimeters for SoilMoist; degrees Celcius for Tair ; and watts per square meter for
net radiation. All abbreviated terms here are referred to in Table 5.
cipitation than that in the Brahmaputra (cc = 0.77, p < 0.05)
and Ganges (cc = 0.82, p < 0.05).
The relationships of evapotranspiration with various atmospheric variables (radiation, air temperature) and soil water
availability are rather complex (Shaaban et al., 2011). Different methods for estimating PET in different hydrological
Hydrol. Earth Syst. Sci., 19, 747–770, 2015
models may also be a source of uncertainty (Thompson et
al., 2014). However, the ET scheme in the H08 model uses
the bulk formula where the bulk transfer coefficient is used
to calculate turbulent heat fluxes (Haddeland et al., 2011). In
estimating PET (and hence ET), H08 uses humidity, air temperature, wind speed and net radiation. Figure 6 presents the
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M. Masood et al.: Hydrology of Ganges–Brahmaputra–Meghna basin
correlation of ET with different meteorological variables in
three basins. The ET in the Brahmaputra has a significant correlation with precipitation, air temperature, specific humidity
and net radiation with the cc ranging from 0.70 to 0.89 (all
of which are statistically significant at p < 0.05). The correlations of ET in the Meghna Basin with the meteorological variables are also relatively strong (cc ranges from 0.61
to 0.80, p < 0.05) except for the net radiation (cc = 0.44,
p < 0.05). However, ET in the Ganges Basin has a weak correlation with the meteorological variables (cc from 0.29 to
0.59, p < 0.05). A weaker correlation of ET with the meteorological variables is likely attributed to the overestimation
of actual ET in the Ganges Basin, because the upstream water use (which is larger in the Ganges) may be incorrectly
estimated as ET by the H08 model to ensure water balance.
4.3
Interannual variability
Figure 7 presents the interannual variability of meteorological and hydrologic variables from simulations driven by using five different GCMs and that of the multimodel mean
(shown by the thick blue line) for three basins. It can be seen
from the figure that the magnitude of interannual variations
of variables corresponding to individual GCMs are noticeably larger than that of the multimodel mean. However, the
long-term trends in the meteorological and hydrologic variables of the multimodel mean are generally similar to that of
each of the GCMs. Figure 7a1–a3 shows that the long-term
trend in precipitation is not pronounced in the Brahmaputra
and Meghna basins, but its interannual variability is rather
large for each GCM. Among the five GCMs used, the precipitation of MRI-AGCM3 has the largest interannual variability
(particularly in the Ganges and Meghna basins). A clear increasing trend in air temperature can be observed for all three
basins. As there is strong correlation between precipitation
and runoff (Fig. 6), the interannual variabilities of them are
similar. There is no clear trend for ET in each basin from the
present to the near-future periods. However, in the far-future
period a notable increasing trend is observed for all basins
(Fig. 7e1–e3). Figure 7f1–f3 plots the interannual variability of soil moisture. Since there are no clear trends (from the
present to the near-future period) identified for precipitation
and evapotranspiration, the effect of climate change on soil
moisture is not pronounced.
4.4
Projected mean changes
The long-term average seasonal cycles of hydrometeorological variables in the two projected periods
(2015–2039 and 2075–2099) were compared with that in the
reference period (1979–2003). All the results presented here
are from the multimodel mean of all simulations driven by
the climate forcing data from five GCMs for both reference
and future periods. The solid lines in Fig. 8 represent the
monthly averages and the dashed lines represent the upper
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759
and lower bounds of the uncertainty bands as determined
from the 10 simulations using the 10 optimal parameter
sets (identified by ranking the NSE). Figure 9 plots the
corresponding percentage changes and Table 6 summarizes
these relative changes in the hydro-meteorological variables
over the three basins on the annual and 6-month (dry season
and wet season) basis.
4.4.1
Precipitation
Considering a high-emissions scenario, by the end of 21st
century the long-term mean precipitation is projected to increase by 16.3, 19.8 and 29.6 % in the Brahmaputra, Ganges
and Meghna basins, respectively (Table 6), in agreement with
previous studies which compared GCM simulation results
over these regions. For example, Immerzeel (2008) estimated
the increase of precipitation in the Brahmaputra Basin as 22
and 14 % under the SRES A2 and B2 scenarios, respectively.
Endo et al. (2012) considered the SRES A1B scenario and estimated the country-wise increase in precipitation as 19.7 and
13 % for Bangladesh and India, respectively. Based on the
present study, for the Brahmaputra and Meghna basins, the
changes in precipitation in the dry season (November–April)
are of 23 and 33.6 %, respectively; both are larger than the
change in wet season (May–October) (Brahmaputra: 15.1 %;
Meghna: 29 %) (Fig. 9b, c). However, the change of precipitation in the dry season in the Ganges Basin (3.6 %) is lower
than that in wet season (21.5 %).
4.4.2
Air temperature
The GBM basin will be warmer by about 1 ◦ C in the
near-future period (Brahmaputra: 1.2 ◦ C; Ganges: 1.0 ◦ C;
Meghna: 0.7 ◦ C) and by about 4.3 ◦ C in the far-future period (Brahmaputra: 4.8 ◦ C; Ganges: 4.1 ◦ C; Meghna: 3.8 ◦ C)
(Table 6). According to the projected changes, the cooler
Brahmaputra Basin will be significantly warmer, with the
maximum increase of up to 5.9 ◦ C in February (Fig. 9d).
In Immerzeel (2008), the increase of air temperature in the
Brahmaputra Basin is projected (under the SRES A2 and B2
scenarios) as 2.3 ∼ 3.5 ◦ C by the end of 21st century. However, the rate of increase over the year is not uniform for all
these basins. Temperature will increase more in winter than
in summer (Fig. 9d–f). Therefore, a shorter winter and an
extended spring can be expected in the future of the GBM
basin, which may significantly affect the crop growing season as well.
4.4.3
Runoff
Long-term mean runoff is projected to increase by 16.2,
33.1 and 39.7 % in the Brahmaputra, Ganges and Meghna
basins, respectively, by the end of the century (Table 6).
Percentage increase of runoff in the Brahmaputra will be
quite large in May (about 36.5 %), which may be due to
the increase of precipitation and also smaller evapotranspiHydrol. Earth Syst. Sci., 19, 747–770, 2015
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M. Masood et al.: Hydrology of Ganges–Brahmaputra–Meghna basin
MIROC-ESM
MIROC5
MRI-AGCM3.2S
MRI-CGCM3
HadGEM2-ES
Multi-model mean
Figure 7. (a1)–(f3) Interannual variation of mean of meteorological and hydrological variables of five GCMs for the present-day (1979–
2003), near-future (2015–2039) and far-future (2075–2099) periods. Thick blue lines represent the means of five GCMs.
ration caused by lower net radiation (Fig. 9g, m). In response to seasonally varying degrees of changes in air temperature, net radiation and evaporation, the changes of runoff
in the wet season (May–October) (Brahmaputra: 20.3 %;
Ganges: 36.3 %; Meghna: 41.8 %) are larger than in the
dry season (November–April) (Brahmaputra: 2.9 %, Ganges:
−2.3 %, Meghna: 24.2 %) (Fig. 9j, k). Runoff in the Meghna
Basin shows a larger response to precipitation increase,
which could lead to higher possibility of floods in this basin
and prolonged flooding conditions in Bangladesh. These
findings are in general consistent with previous findings.
Mirza (2002) reported that the probability of occurrence of
20-year floods is expected to be higher in the Brahmaputra
and Meghna rivers than in Ganges river. However, Mirza et
al. (2003) found that future change in the peak discharge of
Hydrol. Earth Syst. Sci., 19, 747–770, 2015
the Ganges River (as well as the Meghna River) is expected
to be larger than that of the Brahmaputra River.
4.4.4
Evapotranspiration
It can be seen from Fig. 9m–o that the change of ET in
the near-future period is relatively low but increases to be
quite large by the end of the century (Brahmaputra: 16.4 %;
Ganges: 13.6 %; Meghna: 12.9 %). This is due to the increase of net radiation (Brahmaputra: 5.6 %; Ganges: 4.1 %;
Meghna: 4.4 %) as well as the higher air temperature. Following the seasonal patterns of radiation (Fig. 9g–i) and
air temperature (Fig. 9d–f), the change of ET is expected
to be considerably larger in the dry season (November–
April) (Brahmaputra: 25.6 %; Ganges: 19.3 %; Meghna:
18.2 %) than that in wet season (May–October) (Brahmaputra: 12.9 %; Ganges: 10.9 %; Meghna: 10.5 %).
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M. Masood et al.: Hydrology of Ganges–Brahmaputra–Meghna basin
CV (cu): 8.8%
CV (nf): 8.9%
CV (ff): 8.7%
CV (cu): 3.2%
CV (nf): 3.0%
CV (ff): 3.1%
761
CV (cu): 1.9%
CV (nf): 1.9%
CV (ff): 1.8%
CV (cu): 2.1%
CV (nf): 2.1%
CV (ff): 2.0%
CV (cu): 8.7%
CV (nf): 8.4%
CV (ff): 7.6%
CV (cu):6.0%
CV (nf):5.0%
CV (ff):4.5%
CV (cu): 3.8%
CV (nf): 3.9%
CV (ff): 3.9%
CV (cu): 8.0%
CV (nf): 8.0%
CV (ff): 8.0%
CV (cu):30.6%
CV (nf):30.5%
CV (ff):30.2%
CV (cu): 11.2%
CV (nf): 10.7%
CV (ff): 9.9%
CV (cu): 18.2%
CV (nf): 18.2%
CV (ff): 18.0%
CV (cu): 15.8%
CV (nf): 15.4%
CV (ff): 14.4%
mean (cu)
upper and lower bound of uncertainty band (cu)
mean (nf)
upper and lower bound of uncertainty band (nf)
mean (ff)
upper and lower bound of uncertainty band (ff)
Figure 8. (a1)–(f3) The mean (solid line), upper and lower bounds (dashed line) of the uncertainty band of the hydrological quantities
and net radiation components for the present-day (black), near-future (green) and far-future (red) simulations as determined found from 10
simulation results considering 10 optimal parameter sets according to NSE (cu: present-day, nf: near-future, ff: far-future). Coefficients of
variations (CVs) for all periods (Table 7) are noted in each subplot.
4.4.5
Soil moisture
Soil moisture is expressed in terms of the water depth per
unit area within the spatially varying soil depths (3–5 m). The
change of soil moisture (ranges from 1.5–6.9 % in the farfuture) is lower compared to other hydrological quantities,
except for the Meghna Basin in April where the soil moisture is projected to increase by 22 %. However, the associated
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uncertainties for all seasons are relatively high compared to
other variables (Fig. 8f1–f3).
4.4.6
Net radiation
Net radiation is projected to increase by > 4 % for all seasons except summer in the entire GBM basin by the end of
the century (Fig. 9g–i). Due to the increase in the future air
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M. Masood et al.: Hydrology of Ganges–Brahmaputra–Meghna basin
Table 6. The 10-simulation average of annual mean and percentage changes of hydrological and meteorological variables.
annual mean
dry season (November–April)
wet season (May–October)
annual
annual mean
dry season (November–April)
wet season (May–October)
annual
Meghna
% change (Tair : ◦ C)
annual
Ganges
% change (Tair : ◦ C)
wet season (May–October)
Brahmaputra
% change (Tair : ◦ C)
dry season (November–April)
Period
annual mean
Variable
Precipitation (mm year−1 )
present-day (1979–2003)
near-future (2015–2039)
far-future (2075–2099)
1632
1720
1897
–
4.2
23.0
–
5.6
15.1
–
5.4
16.3
1154
1218
1383
–
−0.1
3.6
–
6.2
21.5
–
5.6
19.8
3192
3598
4139
–
11.4
33.6
–
12.9
29.0
–
12.7
29.6
Tair (◦ C)
present-day (1979–2003)
near-future (2015–2039)
far-future (2075–2099)
5.5
6.7
10.3
–
1.4
5.5
–
1.0
4.1
–
1.2
4.8
21.7
22.8
25.9
–
1.1
4.6
–
0.9
3.7
–
1.0
4.1
23.0
23.7
26.8
–
0.8
4.3
–
0.6
3.4
–
0.7
3.8
Net radiation (W m−2 )
present-day (1979–2003)
near-future (2015–2039)
far-future (2075–2099)
63
62
66
–
2.0
10.3
–
−1.6
3.1
–
−0.4
5.6
97
97
101
–
−0.2
5.3
–
−0.9
3.4
–
−0.7
4.1
114
112
119
–
−0.4
6.5
–
−2.2
3.0
–
−1.5
4.4
Total runoff (mm year−1 )
present-day (1979–2003)
near-future (2015–2039)
far-future (2075–2099)
1166
1244
1355
–
0.5
2.9
–
8.6
20.3
–
6.7
16.2
372
414
495
–
2.5
−2.3
–
12.1
36.3
–
11.3
33.1
1999
2380
2793
–
10.5
24.2
–
20.2
41.8
–
19.1
39.7
ET (mm year−1 )
present-day (1979–2003)
near-future (2015–2039)
far-future (2075–2099)
467
477
543
–
5.5
25.6
–
0.9
12.9
–
2.1
16.4
785
808
892
–
4.9
19.3
–
2.1
10.9
–
3.0
13.6
1193
1216
1347
–
5.2
18.2
–
0.4
10.5
–
1.9
12.9
Soil moisture (mm)
present-day (1979–2003)
near-future (2015–2039)
far-future (2075–2099)
335
338
340
–
0.4
0.2
–
1.2
2.3
–
0.9
1.5
186
192
197
–
2.7
0.4
–
3.4
8.3
–
3.1
5.8
336
354
359
–
6.6
6.7
–
5.1
6.9
–
5.5
6.9
(a) Meteorological variables
(b) Hydrological variables
Table 7. Statistical indices (the CV and SD) of the uncertainty in model simulations due to the uncertainty in model parameters.
Variable
Period
Brahmaputra
Ganges
Meghna
Coefficient of
variation (CV) of
mean (Fig. 8) (%)
Standard deviation
(SD) of mean
(Fig. 8)
Coefficient of
variation (CV) of
mean (Fig. 8) (%)
Standard deviation
(SD) of mean
(Fig. 8)
Coefficient of
variation (CV) of
mean (Fig. 8) (%)
Standard deviation
(SD) of mean
(Fig. 8)
Net radiation
present-day
near-future
far-future
8.6
8.6
8.4
5.4
5.4
5.6
2.0
1.9
1.8
2.0
1.9
1.8
2.1
2.1
2.0
2.4
2.3
2.4
Total runoff
present-day
near-future
far-future
3.2
3.0
3.1
0.1
0.1
0.1
7.6
7.2
6.6
0.1
0.1
0.1
6.7
5.4
4.6
0.4
0.4
0.4
ET
present-day
near-future
far-future
7.9
7.9
7.8
0.1
0.1
0.1
3.6
3.7
3.7
0.1
0.1
0.1
11.3
10.6
9.7
0.4
0.4
0.4
Soil moisture
present-day
near-future
far-future
31.0
30.8
30.5
103.7
104.1
103.7
18.5
18.5
18.3
34.5
35.5
36.1
15.9
15.4
14.4
53.5
54.5
51.6
temperature, the downward long-wave radiation would increase accordingly and lead to the increase in net radiation.
However, the change of net radiation in the far-future period
is larger in the dry season (Brahmaputra: 10.3 %; Ganges:
5.3 %; Meghna: 6.5 %) than in the wet season (Brahmaputra:
3.1 %; Ganges: 3.4 %; Meghna: 3 %). For the near-future period, net radiation is projected to decrease by < 1 % through
Hydrol. Earth Syst. Sci., 19, 747–770, 2015
almost all seasons due to the smaller increase in air temperature (∼ 1 ◦ C) as well as decreased incoming solar radiation
(not shown) in this basin.
4.5
Uncertainty in projection due to model parameters
In recent decades, along with the increasing computational
power there has been a trend towards increasing complexity
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763
Figure 9. (a)–(r) Percentage changes in the monthly means of the climatic and hydrologic quantities from the present-day period to the
near-future and far-future periods. The dashed lines represent the annual mean changes.
of hydrological models to capture natural phenomena more
precisely. However, the increased complexity of hydrological
models does not necessarily improve their performance for
unobserved conditions due to the uncertainty in the model
parameter values (Carpenter and Georgakakos, 2006; Tripp
and Niemann, 2008). An increase in complexity may improve the calibration performance due to the increased flexibility in the model behavior, but the ability to identify correct parameter values is typically reduced (Wagener et al.,
2003). Model simulations with multiple combinations of parameter sets can perform equally well in reproducing the observations. Another source of uncertainty comes from the assumption of stationary model parameters, which is one of the
major limitations in modeling the effects of climate change.
Model parameters are commonly estimated under the current
climate conditions as a basis for predicting future conditions,
but the optimal parameters may not be stationary over time
(Mirza and Ahmad, 2005). Therefore, the uncertainty in future projections due to model parameter specification can be
critical (Vaze et al., 2010; Merz et al., 2011; Coron et al.,
www.hydrol-earth-syst-sci.net/19/747/2015/
2012), although it is usually ignored in most climate change
impact studies (Lespinas et al., 2014). Results obtained by
Vaze et al. (2010) indicated that the model parameters can
generally be used for climate impact studies when a model
is calibrated using more than 20 years of data and where
the future precipitation is not more than 15 % lower or 20 %
higher than that in the calibration period. However, Coron
et al. (2012) found a significant number of errors in simulations due to this uncertainty and suggested further research
to improve the methods of diagnosing parameter transferability under the changing climate. For the purpose of minimizing this parameter uncertainty, the average results from the
10 simulations using 10 optimal parameter sets are considered as the simulation result for the two future periods in this
study. Also, the propagating uncertainty in simulation results
due to the uncertainty in mode parameters will be quantified and compared among various hydrologic variables in this
study.
The upper and lower bounds of the uncertainty of hydrometeorological variables are plotted in Fig. 8 for all the simHydrol. Earth Syst. Sci., 19, 747–770, 2015
764
M. Masood et al.: Hydrology of Ganges–Brahmaputra–Meghna basin
ulation periods. It can be seen from the figure that the uncertainty band of runoff is relatively narrow, which indicates
that future runoff is well predictable through model simulations. The uncertainty due to model parameters in runoff projection is lower (the CV ranges between 3 and 7.6 % among
three basins) than that of other hydrologic variables (Table 7,
Fig. 8d1–d3). In addition, in Fig. 4e it is observed that there
is no significant uncertainty in simulated peak discharge for
the Brahmaputra and Meghna rivers. Lower uncertainty in
simulating runoff is highly desirable for climate change impact studies; for instance, the flood risk assessment where the
runoff estimate (especially the peak flow) is the main focus.
However, a relatively wide uncertainty band of runoff can be
found in the Ganges Basin during the wet season (Fig. 8d2),
which might be due to the fact that the upstream water use
(diversion) in the Ganges was not well represented in the
model. Notice that the lower uncertainty in runoff projection
relative to other variables could be expected as the model was
calibrated and validated against observed streamflow at the
basin outlet. The uncertainty in ET projection is also lower
(CV: 3.6–11.3 %; SD: 0.1–0.4), which can be related to the
narrower uncertainty band of net radiation (CV: 1.8–8.6 %;
SD: 1.8–5.6). On the other hand, the projection of soil moisture is rather uncertain for all three basins (CV: 14.4–31 %;
SD: 35–104). Large uncertainty in predicting soil moisture
can be a serious issue which is significant in land use management and agriculture. This emphasizes the critical significance of (1) suitable parameterization of soil water physics in
the model, (2) a reliable regional soil map for the specification of model parameters, and (3) soil moisture observations
for model calibration and validation.
5
Conclusions
This study presents model analyses of the climate change
impact on the Ganges–Brahmaputra–Meghna (GBM) basin
focusing on (1) the setup of a hydrologic model by integrating the fine-resolution (∼ 0.5 km) DEM data for the accurate
river network delineation to simulate at relatively fine grid
resolution (10 km) (2) the calibration and validation of the
hydrologic model with long-term observed daily discharge
data and (3) the impacts of future climate changes in the
basin-scale hydrology. The uncertainties in the future projection stemming from model parameters were also assessed.
The time-slice numerical experiments were performed using
the model forced by the climatic variables from five GCMs
(all participating in the CMIP5) for the present-day (1979–
2003), near-future (2015–2039) and the far-future (2075–
2099) periods.
The following findings and conclusions were drawn from
the model analysis:
– (a) The entire GBM basin is projected to be warmer,
in the range of 1–4.3 ◦ C in the near-future and farfuture periods. The cooler Brahmaputra Basin will be
Hydrol. Earth Syst. Sci., 19, 747–770, 2015
warmer than the Ganges and Meghna. (b) Considering a high-emissions scenario, by the end of 21st century the long-term mean precipitation is projected to increase by +16.3, +19.8 and +29.6 %, and the long-term
mean runoff is projected to increase by +16.2, +33.1
and +39.7 % in the Brahmaputra, Ganges and Meghna
basins, respectively. (c) The change of ET in the nearfuture is relatively low, but increases to be quite large
by the end of the century due to the increase of net radiation as well as the higher air temperature. However,
the change will be considerably larger in the dry season
than in the wet season. (d) The change of soil moisture
is lower compared to other hydrological quantities.
– Overall, it is observed that the climate change impact
on the hydrological processes of the Meghna Basin is
larger than that in the other two basins. For example,
in the near-future period runoff in the Meghna Basin
is projected to increase by 19.1 % whereas it is projected to increase by 6.7 and 11.3 % for the Brahmaputra and Ganges, respectively. In the far-future period,
a larger increase of precipitation (29.6 %), lower increase of ET (12.9 %) and consequently larger increase
of runoff (39.7 %) lead to a higher possibility of floods
in this basin.
– The uncertainty due to model parameters in runoff projection is lower than that of other hydrologic variables.
The uncertainty in ET projection is also lower, which
can be related to the narrower uncertainty band of net
radiation. On the other hand, the projection of soil moisture is rather uncertain in all three basins, which can be
significant in land use management and agriculture in
particular. This emphasizes the significance of (1) suitable parameterization of soil water physics in the model,
(2) a reliable regional soil map for the specification of
model parameters, and (3) soil moisture observations
for model calibration and validation.
However, this study still has some limitations which can be
addressed in future research. (a) All results presented here are
basin-averaged. The basin-averaged large-scale changes and
trends are difficult to translate to regional- and local-scale
impacts. Moreover, the changes in averages do not reflect the
changes in variability and extremes, (b) anthropogenic and
industrial water use upstream are important factors in altering the hydrologic cycle; however, they were not considered
in the present study due to data constraints, (c) urbanizing
watersheds are characterized by rapid land use changes and
the associated landscape disturbances can shift the rainfall–
runoff relationships away from natural processes. Hydrological changes in future can also be amplified by changing land
uses. However, in our study future changes of demography
and land uses were not considered.
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765
Appendix A: Model validation at three upstream station
The model performance was further evaluated by comparing the simulated monthly streamflow with the observed data
from the Global Runoff Data Centre (GRDC) at three upstream gauging stations (Farakka, Pandu and Teesta) in the
GBM basin. The locations and drainage areas of these three
stations are summarized in Table 3. Although the available
data period does not cover the study period 1980–2001 (except for the Teesta which has the data from 1985 to 1991),
the mean seasonal cycle and the mean, maximum, minimum,
and the standard deviation of the streamflow are compared in
Fig. A1 and Table A1. It can be seen that the mean seasonal
cycle of simulated streamflow matches well with the corresponding GRDC data (Fig. A1d–f). Also, the agreement of
the simulated and observed 1985–1991 monthly streamflow
at the Teesta station of the Brahmaputra Basin is excellent
(Fig. A1c).
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M. Masood et al.: Hydrology of Ganges–Brahmaputra–Meghna basin
Table A1. Comparison between observed (data source: GRDC) and simulated discharge (m3 s−1 ) at the Farakka gauging station in the
Ganges Basin, and Pandu and Teesta stations in the Brahmaputra Basin.
Basin
Station
Data type
Data period
(with missing)
Mean
Maximum
Minimum
Standard deviation
Ganges
Farakka
Brahmaputra
Pandu
Brahmaputra
Teesta
observed
1949–1973
simulated
1980–2001
observed
1975–1979
simulated
1980–2001
observed
1969–1992
simulated
1980–2001
12 037
65 072
1181
14 762
11 399
69 715
414
15 518
18 818
49 210
4367
12 073
15 868
46 381
3693
11 709
915
3622
10
902
920
4219
122
948
Figure A1. Comparisons between simulated (magenta line) and observed GRDC (blue line) data for (a)–(c) the monthly time series of
discharges and (d)–(f) long-term mean seasonal cycles at the Farakka gauging station in the Ganges Basin and the Pundu and Teesta stations
in the Brahmaputra Basin.
Hydrol. Earth Syst. Sci., 19, 747–770, 2015
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767
Appendix B
Table B1. Salient features of CMIP5 climate models used in the
analysis.
Model name
MIROC-ESM
MIROC5
MRI-AGCM3.2S
MRI-CGCM3
HadGEM2-ES
Modeling center
Japan Agency for MarineEarth Science and Technology, Atmosphere and Ocean
Research Institute (The University of Tokyo), and National Institute for Environmental Studies
Atmosphere and Ocean Research Institute (The University of Tokyo), National
Institute for Environmental
Studies, and Japan Agency
for Marine-Earth Science
and Technology
Meteorological
Research
Institute
(MRI), Japan and
Japan Meteorological
Agency (JMA), Japan
Meteorological
Research Institute
(MRI), Japan
Met Office Hadley
Centre, UK
Scenario
Nominal horizontal resolution
Model type
Aerosol component name or type
Atmospheric Chemistry
Land surface component
Ocean Biogeochemistry
Sea ice
RCP 8.5
2.81 × 2.77◦
ESMa
SPRINTARS
Not implemented
MATSIRO
NPZD-type
Included
RCP 8.5
1.41 × 1.39◦
ESMa
SPRINTARS
Not implemented
MATSIRO
Not implemented
Included
SRES A1B
0.25 × 0.25◦
AMIPb
Prescribed
Not implemented
SiB0109
Not implemented
Not implemented
RCP 8.5
1.125 × 1.11◦
ESMa
RCP 8.5
1.875 × 1.25◦
ESMa
Interactive
Included
Included
Included
Included
Not implemented
HAL
Not implemented
Included
a ESM is Earth system model. Atmosphere–ocean general circulation models (AOGCMs) with representation of biogeochemical cycles. b AMIP refers to models with atmosphere and land surface only, using observed sea
surface temperature and sea ice extent.
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M. Masood et al.: Hydrology of Ganges–Brahmaputra–Meghna basin
Acknowledgements. This study is supported by the Public Works
Research Institute (PWRI), Japan. The first author is indebted to
the authority of Nippon Koei Co., Ltd., Japan, for the grant from
The Kubota Fund. Also, thanks are given to A. Hasegawa, and
T. Sayama for help in data preparation and for suggestions, and to
the Bangladesh Water Development Board (BWDB) for providing
observed hydrological data. Finally, the authors wish to thank
the editor and five anonymous reviewers for their constructive
comments and suggestions, which greatly improved the quality of
the paper.
Edited by: F. Tian
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